CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Error Reduction based Demand Forecasting: An Appraisal of Kerala Power System

by K. Pramelakumari, V. P. Jagathy Raj, P. S. Sreejith
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 11
Year of Publication: 2018
Authors: K. Pramelakumari, V. P. Jagathy Raj, P. S. Sreejith
10.5120/ijca2018917726

K. Pramelakumari, V. P. Jagathy Raj, P. S. Sreejith . Error Reduction based Demand Forecasting: An Appraisal of Kerala Power System. International Journal of Computer Applications. 182, 11 ( Aug 2018), 10-15. DOI=10.5120/ijca2018917726

@article{ 10.5120/ijca2018917726,
author = { K. Pramelakumari, V. P. Jagathy Raj, P. S. Sreejith },
title = { Error Reduction based Demand Forecasting: An Appraisal of Kerala Power System },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 182 },
number = { 11 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number11/29863-2018917726/ },
doi = { 10.5120/ijca2018917726 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:07.253280+05:30
%A K. Pramelakumari
%A V. P. Jagathy Raj
%A P. S. Sreejith
%T Error Reduction based Demand Forecasting: An Appraisal of Kerala Power System
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 11
%P 10-15
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Demand forecasting is a procedure for identifying the electrical demand that can be expected from a specified number of consumers in a specified period. Electrical demand and electrical supply system could be in terms of average system demand, maximum system demand, and load demand in MW or energy demand in MWhr. Demand forecasting is usually undertaken with the prediction of hourly, daily, weekly and annually of the system demand and peak demand. Categorized form of load forecasting are short-term, long-term and medium-term which takes a few hours ahead of few weeks, one week to one year and five years to twenty years etc. This paper describes the demand forecasting of Kerala power system by using two different methods and a comparative analysis on its impact on the accuracy of load forecasting. This paper also present an error reduction based demand forecasting of Kerala power system. The performance evaluation parameters MAPE, MSE, RMSE, MAE/MAD and percentage error have been used for testing this proposed forecasting models with error reduction strategies.

References
  1. A.Obok Opok, G.O. Anderson and K.M. Yanev, “Electricity Demand Forecasting Towards Vision 2016 for Botswana Using Decomposition Time Serial Model,” International Journal of Power and Energy Systems,Vol.28,No.2, pp.229-237, 2008.
  2. P. R. J. Campbell and K. Adamson, “Methodologies for Load Forecasting,” 3rd International IEEE Conference Intelligent Systems, September, pp. 800–806, 2006.
  3. Economic Review, 1997, State Planning Board, Government of Kerala.
  4. D. C. Park, M. El-Sharkawi, R. Marks, A. Atlas and M. Damborg, “Electric Load Forecasting Using an Artificial Neural Network.” IEEE Trans. on Power Systems, Vol. 6, No. 2, pp. 442-449, May 1991.
  5. Mohan B. Tasre, Vilas N. Ghate and Prashant P. Bedkar, “Hourly Load Forecasting Using Artificial Neural Network for a Small Area,”IEEE International Conference on Advances in Engineering, Science and Management, pp.379-385, 2012.
  6. V. Shrivastava, R. B. Misra, “A Novel Approach of Input Variable Selection for ANN Based Load Forecasting,” Power System Technology and IEEE Power India Conference, p. 5, 2008.
  7. Amit Jain, IEEE and B. Satish, “Short-Term Load Forecasting by Clustering Technique Based on Daily Average and Peak Loads,” IEEE Conference,p.6, 2009.
  8. W. Charytoniuk and Mo-Shing Chen, “Short-Term Load Forecasting Using Artificial Neural Networks,” IEEE Transactions on Power Systems, Vol.15, No. 1, pp. 263-268, 2000.
  9. Carolina Gar`cia-Ascanio, Carlos Mate, “Electric Power Demand Forecasting Using Interval Time Series: A Comparison between VAR and iMLP,” Energy Policy, Vol.38, pp.715-725, 2010.
  10. I. Aquino, C. Perez, J.K. Chavez and S. Oporto, “Daily Load Forecasting Using Quick Propagation Neural Network with a Special Holiday Encoding,” Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12-17, 2007.
  11. B. J. Chen, M.W. Chang and C. Jen Lin, “Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001,” Electrical Power and Energy System, Vol. 26 No.2,pp 1-7.
  12. George C. S. Wang, “ Forecasting Practices in Electric and Gas Utility Companies,” The Journal of Business Forecasting Methods & Systems, Spring, pp.11-13, 2004.
  13. N.Hodzic, T.Konjic and V.Miranda, “Artificial Neural Networks Applied to Short Term Load Diagram Prediction,” 8th Seminar on Neural Network Applications in Electrical Engineering, pp. 219–223, 2006.
  14. K. Y. Lee, Y. T. Cha and J. H. Park,”Short-term Load Forecasting using an Artificial Network,”IEEE Trans. on Power Systems, Vol. 7, No.1, pp. 124-132, Feb. 1992.
Index Terms

Computer Science
Information Sciences

Keywords

Demand forecasting optimization neural network and time series.

Powered by PhDFocusTM